Oriented and Directional Chamfer Distance Losses for 3D Object Reconstruction From a Single Image
Jinxiao Lu, Zhizhen Li, Jiquan Bai, Qian Yu
Abstract
The application of deep learning in the field of 3D reconstruction has greatly improved the quality of 3D object reconstruction. For methods that take the point cloud as supervision information, previous research has mainly focused on the network architecture while setting Chamfer Distance (CD) loss as the default loss function. However, CD only contains distance information while ignoring directional information. In this paper, we introduce novel CD losses considering directions that can be used in a 3D reconstruction network. These CD losses consider both direction and distance information, and have two specific variants, Oriented Chamfer Distance (OCD) and Directional Chamfer Distance (DCD). Numerous experiments conducted on the deformable patch and point cloud reconstruction, show that some classic neural networks for 3D reconstruction with OCD or DCD loss can achieve better reconstruction results than those with CD loss.